Apparatus and method to enhance a contrast using histogram matching

ABSTRACT

An apparatus and method to enhance a contrast includes a first operation part, a second operation part, and a mapping part. The first operation part calculates an average and a standard deviation of an input image. The second operation part calculates an average and a standard deviation of a target image based on the average and the standard deviation of the input image. The mapping part converts a pixel value of the input image by a mapping function generated by receiving the averages and the standard deviations of the input image and the target image from the first operation part and the second operation part, respectively, and outputs a pixel value of an output image.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the priority of Korean Patent ApplicationNo. 2002-6832, filed Feb. 6, 2002 in the Korean Intellectual PropertyOffice, the disclosure of which is incorporated herein by reference.

BACKGROUND OF THE INVENTION

[0002] 1. Field of the Invention

[0003] The present invention relates to an apparatus and method toenhance a contrast using a histogram matching, and more particularly, toan apparatus and method to enhance a contrast of an output image using amapping table written based on a distribution of an input image and atarget image.

[0004] 2. Description of the Related Art

[0005] A histogram is to express a distribution of contrast values ofpixels of an image. In other words, the histogram expresses adistribution range and values of light points and dark points existingin the image. A smoothness of the histogram makes uniform thedistribution of the contrast values with a biased image or a non-uniformimage, to thereby enhance the image. Through the smoothness of thehistogram, a dark image becomes light, and a too light image becomesdark slightly, thereby maintaining a proper lightness. In other words,by correcting the distribution of the lightness values after theconversion, a contrast balance of the image is improved.

[0006]FIG. 1A is a block diagram of a conventional histogram smoothingapparatus 100. Referring to FIG. 1A, the conventional histogramsmoothing apparatus 100 includes a distribution operation part 110 and asmoothing part 120.

[0007] The distribution operation part 110 counts a lightness level froman input image to thereby obtain a histogram value or a probabilitydensity function, and calculates a cumulative distribution function fromthe obtained histogram value and the probability density function. Thesmoothing part 120 makes uniform the histogram of a given image usingthe cumulative distribution function calculated by the distributionoperation part 110. As a consequence, the cumulative distributionfunction is non-linearly mapped with the given image, so that thecontrast of the image is enhanced.

[0008]FIG. 1B is a block diagram of a conventional contrast stretchingapparatus 150. Referring to FIG. 1B, the conventional contraststretching apparatus 150 includes a distribution operation part 160 anda stretching part 170.

[0009] The distribution operation part 160 counts a lightness level fromthe image to thereby obtain the histogram value or the probabilitydensity function. The stretching part 170 obtains a pixel value having alowest value and a highest value using the probability density functionobtained by the distribution operation part 160, and displays thehistogram using the obtained pixel values so as to utilize thedistribution of the contrast values of the image to a maximum extent.

[0010] By performing a subtraction operation using the pixel valuehaving the lowest value in the image, the histogram is moved toward aleft of the image. At this time, the respective pixel values of theimage are extended so as to include the entire contrast values.Accordingly, the image comes to have the contrast values from 0 to 255,so that the contrast of the image increases.

[0011] However, the conventional histogram smoothing has drawbacks suchas being difficult to control a degree of the contrast enhancement. Theconventional histogram utilizes the probability density function and thecumulative distribution function obtained from the given image as amapping to enhance the contrast. Thus, in case that the given image hasa special property or is damaged due to noises, it is not easy to obtaina desired result.

[0012] Also, the conventional histogram smoothing fails to maintain arelative brightness. The brightness of the image obtained through thehistogram smoothing has no relation with the brightness of the givenimage. So, in case that the conventional histogram smoothing is appliedto a video sequence, a discrimination of a light scene and a dark scenedisappears. The conventional histogram smoothing can be effectivelyperformed when it is applied to the image having fineness in a darkportion of the image. On the contrary, the image having a good qualitybecomes bad. In order to overcome the aforementioned drawbacks,complicated algorithms have been provided making it difficult to realizesuch a device.

[0013] Meanwhile, in case of the conventional contrast stretchingtechnology, if there exists a light portion or a dark portion in theimage, a margin of the stretching is not secured, making it difficult toanticipate a good result.

SUMMARY OF THE INVENTION

[0014] Various aspects and advantages of the invention will be set forthin part in the description that follows and, in part, will be obviousfrom the description, or may be learned by practice of the invention.

[0015] Accordingly, it is an aspect of the invention to provide anapparatus and method to enhance a contrast, in which an enhancementdegree of the contrast can be controlled and a relative brightness canbe maintained upon controlling the contrast.

[0016] To accomplish the above aspect and other advantages, there isprovided an apparatus to enhance a contrast. The apparatus includes: afirst operation part calculating an average and a standard deviation ofan input image; a second operation part calculating an average and astandard deviation of a target image based on the average and thestandard deviation of the input image; and a mapping part converting apixel value of the input image by a mapping function generated byreceiving the averages and the standard deviations of the input imageand the target image from the first operation part and the secondoperation part, respectively, and outputting a pixel value of an outputimage.

[0017] In accordance with an aspect of the present invention, theapparatus further includes a mapping range designation part providingthe mapping part with a lowest upper bound and a highest lower boundwith respect to the pixel value of the input image. The mapping partconverts the pixel value of the input image, which exists between thelowest upper bound and the highest lower bound, by the mapping function.

[0018] Selectively, the mapping part converts the pixel value of theinput image using the mapping function selected from a plurality ofmapping functions for respective lower regions formed, by dividing amapping region between the lowest upper bound and the highest lowerbound based on an average of the input image.

[0019] In accordance with an aspect of the present invention, themapping part selects the mapping function used in converting the pixelvalue of the input image from the plurality of mapping functions using afollowing equation: ${F(x)} = \left\{ \begin{matrix}{{\max \left( {{{line}\quad 1},{{line}2}} \right)},{if},{x \leq \quad p_{a}}} \\{{\min \left( {{{line}2},{{line}3}} \right)},{if},{x > p_{a}}}\end{matrix} \right.$

[0020] where, line 1=a(x−threshold_low)+threshold_low${{{line}\quad 2} = \left( {{\frac{\sigma_{b}}{\sigma_{a}}\left( {x - p_{a}} \right)} + p_{b}} \right)},\quad {and}$

[0021] line 3=b(x−threshold_high)+threshold_high.

[0022] Also, the mapping function is expressed by a following equation:${y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}},$

[0023] where, x is the pixel value of the input image, y is the pixelvalue of the output image, σ_(a) is the standard deviation of the inputimage, σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.

[0024] In accordance with an aspect of the present invention, the secondoperation part calculates the standard deviation of the target imageusing a following equation:

σ_(b) =m(1−k)+kσ _(a)

k=g(ρ_(a)−128)

[0025] where, x is the pixel value of the input image, y is the pixelvalue of the output image, σ_(a) is the standard deviation of the inputimage, σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage,

[0026] According to another aspect of the present invention, there isprovided a method to enhance a contrast. The method includes:calculating an average and a standard deviation of an input image;calculating an average and a standard deviation of a target image basedon the average and the standard deviation of the input image; generatinga mapping function based on the average and the standard deviation ofthe input image and the average and the standard deviation of the targetimage; and converting a pixel value of the input image using thegenerated mapping function and outputting a pixel value of an outputimage.

[0027] In an aspect of the present invention, the method, prior to theconversion of the pixel value, further includes designating a lowestupper bound and a highest lower bound with respect to the pixel value ofthe input image. The generation of the mapping function converts thepixel value of the input image exists between the lowest upper bound andthe highest lower bound.

[0028] In an aspect according to the present invention, the conversionof the pixel value of the input image using the mapping functionselected from a plurality of mapping functions for respective lowerregions is formed by dividing a mapping region between the lowest upperbound and the highest lower bound based on the average of the inputimage.

[0029] Here, the conversion of the pixel value selects the mappingfunction used in converting the pixel value of the input image from theplurality of mapping functions using a following equation:${F(x)} = \left\{ \begin{matrix}{{\max \left( {{{line}\quad 1},{{line}2}} \right)},{if},{x \leq \quad p_{a}}} \\{{\min \left( {{{line}2},{{line}3}} \right)},{if},{x > p_{a}}}\end{matrix} \right.$

[0030] where, line 1=a(x−threshold_low)+threshold_low${{{line}\quad 2} = \left( {{\frac{\sigma_{b}}{\sigma_{a}}\left( {x - p_{a}} \right)} + p_{b}} \right)},\quad {and}$

[0031] line 3=b(x−threshold_high)+threshold_high.

[0032] Also, the mapping function is expressed by a following equation:$y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}$

[0033] where, x is the pixel value of the input image, y is the pixelvalue of the output image, σ_(a) is the standard deviation of the inputimage, σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.

[0034] In an aspect according to the present invention, the calculationof the average and the standard deviation of the target image calculatesthe standard deviation of the target image using a following equation:

σ_(b) =m(1−k)+kσ _(a)

k=g(ρ_(a)−128)

[0035] where, σ_(a) is the standard deviation of the input image, ρ_(a)is the average of the input image, m is a predetermined variable of astandard deviation, k is a parameter between 0 and 1, and g( ) is afunction to determine the parameter k, wherein the parameter k convergesto 0 as the average of the input image approaches 128 that is an averageof a Gaussian distribution, and the parameter k converges to 1 as theaverage of the input image is distant from the average of the Gaussiandistribution.

[0036] According to an aspect of the present invention, a contrastenhancement apparatus can be easily realized. Also, by correctingparameters of the target image, an enhancement degree in a contrast ofan entire image can be controlled, and by using multiple mappingfunctions, there can be prevented a phenomenon in which a dark portionand a light portion are overemphasized.

[0037] These together with other aspects and advantages which will besubsequently apparent, reside in the details of construction andoperation as more fully hereinafter described and claimed, referencebeing had to the accompanying drawings forming a part thereof, whereinlike numerals refer to like parts throughout.

BRIEF DESCRIPTION OF THE DRAWINGS

[0038] These and/or other aspects and advantages of the invention willbecome apparent and more readily appreciated from the followingdescription of the embodiments, taken in conjunction with theaccompanying drawings of which:

[0039]FIG. 1A is a block diagram of a conventional histogram smoothingapparatus;

[0040]FIG. 1B is a block diagram of a conventional contrast stretchingapparatus;

[0041]FIG. 2 is a block diagram of a contrast enhancement apparatus, inaccordance with an aspect of the present invention;

[0042]FIG. 3 is a schematic view showing one example of a function, g( )to determine a parameter to approach a proper standard deviationdepending on an average of an input image;

[0043]FIG. 4 is a graph illustrating a method using a mapping functionby selecting one line among multiple lines representing the mappingfunction to prevent a number of pixel values from being mapped to onevalue; and

[0044]FIG. 5 is a flow chart illustrating a contrast enhancement method,in accordance with another aspect of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

[0045] Reference will now be made in detail to the embodiments of thepresent invention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout. The embodiments are described below in order to explain thepresent invention by referring to the figures.

[0046]FIG. 2 is a block diagram of a contrast enhancement apparatus 200,in accordance with an aspect of the present invention.

[0047] Referring to FIG. 2, the contrast enhancement apparatus 200includes a first operation part 210, a second operation part 220, amapping part 230, and a mapping range designation part 240.

[0048] The first operation part 210 calculates an average and a standarddeviation of an input image. The second operation part 220 calculates anaverage and a standard deviation of a target image based on the averageand the standard deviation of the input image. The second operation part220 calculates the standard deviation of the target image using thefollowing Equation 1:

σ_(b) =m(1−k)+kσ _(a),

k=g(ρ_(a)−128)   Equation 1,

[0049] where, σ_(a) is the standard deviation of the input image andρ_(a) is the average of the input image. Further, m is a variable of astandard deviation, and a change of m enables control of a contrastlevel. According to an aspect of the present invention, when a value ofm is 56, the best image is output. The value of m may be changed from anoutside source depending on a user's selection.

[0050] When the standard deviation of a histogram obtained from theinput image is small, that indicates that a distribution of pixels leanstoward one region and that a contrast of the image is not high as awhole. Hence, elevating the standard deviation of the target image usinga histogram matching provides an effect in which the distribution of thepixels is made uniform.

[0051] Further, according to Equation 1, the standard deviation of thetarget image is varied with the value of k, which is calculated by thefunction of g( ). The parameter k converges to zero (0) as the averageof the input image approaches 128, which is an average of a Gaussiandistribution, and the parameter k converges to 1 as the average of theinput image is distant from the average of the Gaussian distribution. Ina case that the parameter k is 0, the variable standard deviationbecomes the standard deviation of the target image, and in case that theparameter k is 1, the standard deviation of the input image becomes thestandard deviation of the target image. In FIG. 3, there is shown oneexample of the function g( ), which is used to express variations of k.

[0052] The mapping part 230 converts a pixel value of the input image bya mapping function generated by receiving the averages and the standarddeviations of the input image and the target image from the firstoperation part 210 and the second operation part 220, and outputs thepixel value of an output image.

[0053] The mapping part 230 converts the input image into the outputimage using the mapping function expressed by the following Equation 2:$\begin{matrix}{{y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}},} & {{Equation}\quad 2}\end{matrix}$

[0054] where, x is the pixel value of the input image, y is the pixelvalue of the output image, σ_(a) is the standard deviation of the inputimage, σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.

[0055] According to Equation 2, when the input image has a Gaussiandistribution of (ρ_(a), σ_(a)), the output image comes to have aGaussian distribution of (ρ_(b), σ_(b)).

[0056] Also, according to Equations 1 and 2, the average of the targetimage uses the average of the input image without a change. In order toincrease the brightness of the target image, the average of the targetimage needs to be increased, and in order to increase the contrastlevel, the standard deviation of the target image needs to be increased.

[0057] The mapping range designation part 240 provides the mapping partwith a lowest upper bound and a the highest lower bound with respect tothe pixel value of the input image to which the mapping function isapplied. Depending on a device as used, there may exist a case in whichthe pixel values of a very dark portion and a very light portion are notchanged. In this case, the mapping range designation part 240 designatescritical values as a threshold low and a threshold high, and providesthe mapping part 230 with the designated critical values. The mappingpart 230 does not apply the mapping with a value in excess of theprovided critical value.

[0058] Furthermore, for the mapping used in the mapping part 230, a casemay occur in which many pixel values are mapped to a single value, whichis prevented by an example to be described in FIG. 4. In other words, arelationship in which three lines representing the mapping function andone line by a relational expression used in respective regions areselected, the selected lines are used in the mapping function. In FIG.4, an example is illustrated in which the relationship is embodied.

[0059] Referring to FIG. 4, when the pixel value “x” is greater than theaverage ρ_(a) of the input image, the smallest of line 2 or line 3 isselected. Also, when the pixel value “x” is equal to or less than theaverage ρ_(a) of the input image, the largest of line 1 or line 2 isselected. The selections are expressed by the following Equation 3:${F(x)} = \left\{ \begin{matrix}{{\max \left( {{{line}\quad 1},{{line}2}} \right)},{if},{x \leq \quad p_{a}}} \\{{\min \left( {{{line}2},{{line}3}} \right)},{if},{x > p_{a}}}\end{matrix} \right.$

[0060] where, line 1=a(x−threshold_low)+threshold_low${{{line}\quad 2} = \left( {{\frac{\sigma_{b}}{\sigma_{a}}\left( {x - p_{a}} \right)} + p_{b}} \right)},\quad {and}$

[0061] line 3=b(x−threshold_high)+threshold_high.

[0062] The mapping part 230 applies the mapping function obtained by theexample illustrated in FIG. 4 to the pixel value of the input image,thereby enhancing the contrast of the image.

[0063]FIG. 5 is a flow chart illustrating a contrast enhancement method,in accordance with an aspect of the present invention.

[0064] Referring to FIG. 5, at operation S500, the first operation part210 calculates the average and the standard deviation for the pixelvalues of the input image. At operation S510, the second operation part220 calculates the average and the standard deviation of the targetimage based on the average and the standard deviation of the inputimage. At operation S510, the average of the target image uses theaverage of the input image.

[0065] At operation S520, the mapping part 230 generates the mappingfunction based on the average and the standard deviation of the inputimage inputted from the first operation part 210 and the average and thestandard deviation of the target image inputted from the secondoperation part 220. Furthermore, at operation S530, the mapping rangedesignation part 240 designates the lowest upper bound and the highestlower bound with respect to the pixel value of the input image to whichthe mapping function is applied, and provides the mapping part 230 withthe lowest upper bound and the highest lower bound. At operation S540,the mapping part 230 applies the mapping function to the pixel existingbetween the lowest upper bound and the highest lower bound provided fromthe mapping range designation part 240 among the pixels of the inputimage, thereby generating the output image.

[0066] As described previously, the present invention provides for aneasy embodiment of a contrast enhancement apparatus. Also, by correctingparameters of a target image, an enhancement degree in a contrast ofentire images can be controlled, and by using multiple mappingfunctions, a phenomenon in which a dark portion and a light portion ofthe image are overemphasized can be prevented.

[0067] The many features and advantages of the invention are apparentfrom the detailed specification and, thus, it is intended by theappended claims to cover all such features and advantages of theinvention that fall within the true spirit and scope of the invention.Further, since numerous modifications and changes will readily occur tothose skilled in the art, it is not desired to limit the invention tothe exact construction and operation illustrated and described, andaccordingly all suitable modifications and equivalents may be resortedto, falling within the scope of the invention.

What is claimed is:
 1. An apparatus enhancing a contrast, the apparatuscomprising: a first operation part calculating an average and a standarddeviation of an input image; a second operation part calculating anaverage and a standard deviation of a target image based o n the averageand the standard deviation of the input image; and a mapping partconverting a pixel value of the input image by a mapping functiongenerated by receiving the averages and the standard deviations of theinput image and the target image from the first operation part and thesecond operation part, respectively, and outputting a pixel value of anoutput image.
 2. The apparatus as claimed in claim 1, furthercomprising: a mapping range designation part providing a mapping partwith a lowest upper bound and a highest lower bound with respect to thepixel value of the input image, wherein the mapping part converts thepixel value of the input image, which exists between the lowest upperbound and the highest lower bound, using the mapping function.
 3. Theapparatus as claimed in claim 2, wherein the mapping part converts thepixel value of the input image using the mapping function selected froma plurality of mapping functions of respective lower regions formed bydividing a mapping region between the lowest upper bound and the highestlower bound based on the average of the input image.
 4. The apparatus asclaimed in claim 3, wherein the mapping part selects the mappingfunction used in converting the pixel value of the input image from theplurality of mapping functions using a following equation:${F(x)} = \left\{ \begin{matrix}{{\max \left( {{{line}\quad 1},{{line}2}} \right)},{if},{x \leq \quad p_{a}}} \\{{\min \left( {{{line}2},{{line}3}} \right)},{if},{x > p_{a}}}\end{matrix} \right.$

where, line 1=a(x−threshold_low)+threshold_low${{line}\quad 2} = {\left( {{\frac{\sigma_{b}}{\sigma_{a}}\left( {x - p_{a}} \right)} + p_{b}} \right),\quad {and}}$

line 3=b(x−threshold_high)+threshold_high.
 5. The apparatus as claimedin claim 2, wherein the mapping function is expressed by a followingequation:$y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}$

where, x is the pixel value of the input image, y is the pixel value ofthe output image, σ_(a) is the standard deviation of the input image,σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.
 6. The apparatus as claimed in claim 2, wherein the secondoperation part calculates the standard deviation of the target imageusing a following equation: σ_(b) =m(1−k)+kσ _(a) k=g(ρ_(a)−128) where,σ_(a) is the standard deviation of the input image, ρ_(a) is the averageof the input image, m is a predetermined variable of a standarddeviation, k is a parameter between 0 and 1, and g( ) is a function todetermine the parameter k, wherein the parameter k converges to 0 as theaverage of the input image approaches 128, which is an average of aGausian distribution, and the parameter k converges to 1 as the averageof the input image is distant from the average of the Gausiandistribution.
 7. The apparatus as claimed in claim 1, wherein themapping function is expressed using a following equation:$y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}$

where, x is the pixel value of the input image, y is the pixel value ofthe output image, σ_(a) is the standard deviation of the input image,σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.
 8. The apparatus as claimed in claim 1, wherein the secondoperation part calculates the standard deviation of the target imageusing a following equation: σ_(b) =m(1−k)+kσ _(a) k=g(ρ_(a)−128) where,σ_(a) is the standard deviation of the input image, ρ_(a) is the averageof the input image, m is a predetermined variable of a standarddeviation, k is a parameter between 0 and 1, and g( ) is a function todetermine the parameter k, wherein the parameter k converges to 0 as theaverage of the input image approaches 128, which is an average of aGaussian distribution, and the parameter k converges to 1 as the averageof the input image is distant from the average of the Gaussiandistribution.
 9. A method to enhance a contrast, the method comprising:calculating an average and a standard deviation of an input image;calculating an average and a standard deviation of a target image basedon the average and the standard deviation of the input image; generatinga mapping function based on the average and the standard deviation ofthe input image and the average and the standard deviation of the targetimage; and converting a pixel value of the input image using thegenerated mapping function and outputting a pixel value of an outputimage.
 10. The method as claimed in claim 9, prior to converting thepixel value of the input image, the method further comprising:designating a lowest upper bound and a highest lower bound with respectto the pixel value of the input image, wherein the generation of themapping function converts the pixel value of the input image, whichexists between the lowest upper bound and the highest lower bound, usingthe mapping function.
 11. The method as claimed in claim 10, wherein thepixel value of the input image is converted by the mapping functionselected from a plurality of mapping functions of respective lowerregions formed by dividing a mapping region between the lowest upperbound and the highest lower bound based on the average of the inputimage.
 12. The method as claimed in claim 11, wherein the mappingfunction used in converting the pixel value of the input image from theplurality of mapping functions is selected using a following equation:${F(x)} = \left\{ \begin{matrix}{{{\max \left( {{line1},\quad {line2}} \right)},\quad {if},\quad x} \leq p_{a}} \\{{{\min \left( {{line2},\quad {line3}} \right)},\quad {if},\quad x} > p_{a}}\end{matrix} \right.$

where, line 1=a(x−threshold_low)+threshold_low${{line}\quad 2} = {\left( {{\frac{\sigma_{b}}{\sigma_{a}}\left( {x - p_{a}} \right)} + p_{b}} \right),\quad {and}}$

line 3=b(x−threshold_high)+threshold_high.
 13. The method as claimed inclaim 10, wherein the mapping function is expressed by a followingequation:$y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}$

where, x is the pixel value of the input image, y is the pixel value ofthe output image, σ_(a) is the standard deviation of the input image,σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.
 14. The method as claimed in claim 10, wherein the standarddeviation of the target image is calculated using a following equation:σ_(b) =m(1−k)+kσ _(a) k=g(ρ_(a)−128) where, σ_(a) is the standarddeviation of the input image, ρ_(a) is the average of the input image, mis a predetermined variable of a standard deviation, k is a parameterbetween 0 and 1, and g( ) is a function to determine the parameter k,wherein the parameter k converges to 0 as the average of the input imageapproaches 128, which is an average of a Gaussian distribution, and theparameter k converges to 1 as the average of the input image is distantfrom the average of the Gaussian distribution.
 15. The method as claimedin claim 9, wherein the mapping function is expressed using a followingequation:$y = {{\left( \frac{\sigma_{b}}{\sigma_{a}} \right)\left( {x - p_{a}} \right)} + p_{b}}$

where, x is the pixel value of the input image, y is the pixel value ofthe output image, σ_(a) is the standard deviation of the input image,σ_(b) is the standard deviation of the target image, ρ_(a) is theaverage of the input image, and ρ_(b) is the average of the targetimage.
 16. The method as claimed in claim 9, wherein the standarddeviation of the target image is calculated using a following equation:σ_(b) =m(1−k)+kσ _(a) k=g(ρ_(a)−128) where, σ_(a) is the standarddeviation of the input image, ρ_(a) is the average of the input image, mis a predetermined variable of a standard deviation, k is a parameterbetween 0 and 1, and g( ) is a function to determine the parameter k,wherein the parameter k converges to 0 as the average of the input imageapproaches 128, which is an average of a Gausian distribution, and theparameter k converges to 1 as the average of the input image is distantfrom the average of the Gausian distribution.